Search Results for author: Hao Dong

Found 81 papers, 39 papers with code

Sym-Q: Adaptive Symbolic Regression via Sequential Decision-Making

no code implementations7 Feb 2024 Yuan Tian, Wenqi Zhou, Hao Dong, David S. Kammer, Olga Fink

Our results demonstrate that Sym-Q excels not only in recovering underlying mathematical structures but also uniquely learns to efficiently refine the output expression based on reward signals, thereby discovering underlying expressions.

Decision Making regression +1

ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation

no code implementations24 Dec 2023 Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, Hao Dong

By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.

Common Sense Reasoning Language Modelling +3

Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers

1 code implementation19 Dec 2023 Ruiyuan Zhang, Jiaxiang Liu, Zexi Li, Hao Dong, Jie Fu, Chao Wu

Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information.

3D Assembly

Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations

no code implementations21 Nov 2023 Yushi Du, Ruihai Wu, Yan Shen, Hao Dong

More importantly, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds of joint motions in the space.

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

1 code implementation20 Nov 2023 Hao Dong, Gaëtan Frusque, Yue Zhao, Eleni Chatzi, Olga Fink

While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.

Data Augmentation Fault Detection +4

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

1 code implementation NeurIPS 2023 Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, Olga Fink

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions.

Contrastive Learning Domain Generalization

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

no code implementations18 Oct 2023 Tianyang Xue, Mingdong Wu, Lin Lu, Haoxuan Wang, Hao Dong, Baoquan Chen

In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling.

Collision Avoidance Layout Design +1

ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection

no code implementations13 Oct 2023 Xiaoqi Li, Yanzi Wang, Yan Shen, Ponomarenko Iaroslav, Haoran Lu, Qianxu Wang, Boshi An, Jiaming Liu, Hao Dong

This framework is designed to capture multiple perspectives of the target object and infer depth information to complement its geometry.

Object Robot Manipulation

Improving Compositional Text-to-image Generation with Large Vision-Language Models

no code implementations10 Oct 2023 Song Wen, Guian Fang, Renrui Zhang, Peng Gao, Hao Dong, Dimitris Metaxas

However, compositional text-to-image models frequently encounter difficulties in generating high-quality images that accurately align with input texts describing multiple objects, variable attributes, and intricate spatial relationships.

Attribute

Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions

no code implementations20 Sep 2023 Yuxing Long, Xiaoqi Li, Wenzhe Cai, Hao Dong

The performances on the representative VLN task R2R show that our method surpasses the leading zero-shot VLN model by a large margin on all metrics.

Language Modelling Large Language Model

Semi-supervised Domain Adaptation in Graph Transfer Learning

no code implementations19 Sep 2023 Ziyue Qiao, Xiao Luo, Meng Xiao, Hao Dong, Yuanchun Zhou, Hui Xiong

To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes.

Semi-supervised Domain Adaptation Transfer Learning +1

Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects

no code implementations NeurIPS 2023 Chuanruo Ning, Ruihai Wu, Haoran Lu, Kaichun Mo, Hao Dong

Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects.

Efficient Exploration Few-Shot Learning

Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly

1 code implementation ICCV 2023 Ruihai Wu, Chenrui Tie, Yushi Du, Yan Zhao, Hao Dong

Shape assembly aims to reassemble parts (or fragments) into a complete object, which is a common task in our daily life.

Disentanglement

GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping

no code implementations12 Sep 2023 Tianhao Wu, Mingdong Wu, Jiyao Zhang, Yunchong Gan, Hao Dong

In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects.

Score-PA: Score-based 3D Part Assembly

1 code implementation8 Sep 2023 Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang, Guanqi Zhan, Chao Wu, Hao Dong

In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly.

Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning

no code implementations6 Sep 2023 Hao Dong, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang, Yuanchun Zhou

Subsequently, we utilize the defined query-aware temporal paths on a history temporal graph to model historical path information related to queries for reasoning.

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

1 code implementation24 Aug 2023 Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Jiaming Liu, Hao Dong, Peng Gao

However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes.

3D Semantic Segmentation Few-shot 3D semantic segmentation +1

GenPose: Generative Category-level Object Pose Estimation via Diffusion Models

no code implementations18 Jun 2023 Jiyao Zhang, Mingdong Wu, Hao Dong

Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings.

6D Pose Estimation 6D Pose Estimation using RGBD +2

Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

1 code implementation18 May 2023 Siyuan Huang, Zhengkai Jiang, Hao Dong, Yu Qiao, Peng Gao, Hongsheng Li

This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks.

Language Modelling Large Language Model +1

Personalize Segment Anything Model with One Shot

1 code implementation4 May 2023 Renrui Zhang, Zhengkai Jiang, Ziyu Guo, Shilin Yan, Junting Pan, Xianzheng Ma, Hao Dong, Peng Gao, Hongsheng Li

Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.

Personalized Segmentation Segmentation +3

Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning

1 code implementation25 Apr 2023 Hao Dong, Zhiyuan Ning, Pengyang Wang, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou, Yanjie Fu

Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently.

Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks

no code implementations29 Mar 2023 Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, Zongqing Lu

Our method outperforms baselines by a large margin and is the most sample-efficient demonstration-free RL method to solve Minecraft Tech Tree tasks.

Multi-Task Learning reinforcement-learning +1

Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation

no code implementations ICCV 2023 Ruihai Wu, Chuanruo Ning, Hao Dong

In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states' values for long-term manipulation.

Deformable Object Manipulation Object

Resilient Binary Neural Network

1 code implementation2 Feb 2023 Sheng Xu, Yanjing Li, Teli Ma, Mingbao Lin, Hao Dong, Baochang Zhang, Peng Gao, Jinhu Lv

In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training.

GFPose: Learning 3D Human Pose Prior with Gradient Fields

1 code implementation CVPR 2023 Hai Ci, Mingdong Wu, Wentao Zhu, Xiaoxuan Ma, Hao Dong, Fangwei Zhong, Yizhou Wang

During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework.

Denoising Monocular 3D Human Pose Estimation +1

SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation

1 code implementation28 Nov 2022 Hao Dong, Xianjing Zhang, Jintao Xu, Rui Ai, Weihao Gu, Huimin Lu, Juho Kannala, Xieyuanli Chen

However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications.

Autonomous Driving Depth Estimation

MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

1 code implementation11 Oct 2022 Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang

A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues.

Multi-agent Reinforcement Learning reinforcement-learning +1

Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors

1 code implementation27 Sep 2022 Hao Dong, Xieyuanli Chen, Mihai Dusmanu, Viktor Larsson, Marc Pollefeys, Cyrill Stachniss

A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.

Dimensionality Reduction Image Retrieval +2

End-to-End Affordance Learning for Robotic Manipulation

1 code implementation26 Sep 2022 Yiran Geng, Boshi An, Haoran Geng, Yuanpei Chen, Yaodong Yang, Hao Dong

Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks.

Reinforcement Learning (RL)

Collaboration of Pre-trained Models Makes Better Few-shot Learner

no code implementations25 Sep 2022 Renrui Zhang, Bohao Li, Wei zhang, Hao Dong, Hongsheng Li, Peng Gao, Yu Qiao

In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning.

Few-Shot Learning Representation Learning

Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification

no code implementations16 Sep 2022 Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du, Pengyang Wang, Hui Xiong, Yuanchun Zhou

Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.

Classification

Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving

no code implementations13 Sep 2022 Hao Dong, Yuya Sasaki

Based on the proposed estimator, we construct a formal test on the sub-unity of the marginal propensity to consume out of permanent income (MPCP) under a nonparametric consumption model and a permanent-transitory model of income dynamics with nonparametric distribution.

Test Unity

TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification

no code implementations2 Sep 2022 Mingdong Wu, Fangwei Zhong, Yulong Xia, Hao Dong

For object rearrangement, the TarGF can be used in two ways: 1) For model-based planning, we can cast the target gradient into a reference control and output actions with a distributed path planner; 2) For model-free reinforcement learning, the TarGF is not only used for estimating the likelihood-change as a reward but also provides suggested actions in residual policy learning.

Imitation Learning Object +2

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

1 code implementation15 Aug 2022 Hao Dong, Xieyuanli Chen, Simo Särkkä, Cyrill Stachniss

We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.

Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects

1 code implementation7 Aug 2022 Qiyu Dai, Jiyao Zhang, Qiwei Li, Tianhao Wu, Hao Dong, Ziyuan Liu, Ping Tan, He Wang

Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks.

Pose Estimation Transparent objects

Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL

no code implementations2 Aug 2022 Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding, Hao Dong, Jun Wang, Yaodong Yang

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community.

Multi-agent Reinforcement Learning

Scalable Model-based Policy Optimization for Decentralized Networked Systems

2 code implementations13 Jul 2022 Yali Du, Chengdong Ma, Yuchen Liu, Runji Lin, Hao Dong, Jun Wang, Yaodong Yang

Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.

Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain

1 code implementation8 Jul 2022 Tong Zhang, Peng Gao, Hao Dong, Yin Zhuang, Guanqun Wang, Wei zhang, He Chen

Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning.

Land Cover Classification object-detection +3

DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation

no code implementations5 Jul 2022 Yan Zhao, Ruihai Wu, Zhehuan Chen, Yourong Zhang, Qingnan Fan, Kaichun Mo, Hao Dong

It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments.

Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

1 code implementation17 Jun 2022 Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuang Jiang, Stephen Marcus McAleer, Yiran Geng, Hao Dong, Zongqing Lu, Song-Chun Zhu, Yaodong Yang

In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.

Few-Shot Learning Offline RL +2

Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals

no code implementations7 Mar 2022 Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du, Pengyang Wang, Dong Li, Yuanchun Zhou

After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.

GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning

no code implementations4 Mar 2022 Tianhao Wu, Fangwei Zhong, Yiran Geng, Hongchen Wang, Yongjian Zhu, Yizhou Wang, Hao Dong

we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it.

Object reinforcement-learning +1

AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions

no code implementations1 Dec 2021 Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas Guibas, Hao Dong

Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.

Friction Test

Fast and Flexible Human Pose Estimation with HyperPose

1 code implementation26 Aug 2021 Yixiao Guo, Jiawei Liu, Guo Li, Luo Mai, Hao Dong

When it comes to customising these algorithms for real-world applications, none of the existing libraries can offer both the flexibility of developing custom pose estimation algorithms and the high-performance of executing these algorithms on commodity devices.

Pose Estimation

VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects

no code implementations ICLR 2022 Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong

In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.

Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

1 code implementation19 Apr 2021 Jie Ren, Yewen Li, Zihan Ding, Wei Pan, Hao Dong

However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).

reinforcement-learning Reinforcement Learning (RL)

Product semantics translation from brain activity via adversarial learning

no code implementations29 Mar 2021 Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo

To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.

EEG Electroencephalogram (EEG) +1

LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

no code implementations22 Feb 2021 Zhiyuan Ning, Ziyue Qiao, Hao Dong, Yi Du, Yuanchun Zhou

Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets.

Knowledge Graph Embedding Knowledge Graphs

P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

no code implementations24 Dec 2020 Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, Hao Dong

Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.

Contrastive Learning Representation Learning +1

End-to-End Object Detection with Adaptive Clustering Transformer

1 code implementation18 Nov 2020 Minghang Zheng, Peng Gao, Renrui Zhang, Kunchang Li, Xiaogang Wang, Hongsheng Li, Hao Dong

In this paper, a novel variant of transformer named Adaptive Clustering Transformer(ACT) has been proposed to reduce the computation cost for high-resolution input.

Clustering Object +2

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

no code implementations30 Sep 2020 Chu-ran Wang, Jing Li, Fandong Zhang, Xinwei Sun, Hao Dong, Yizhou Yu, Yizhou Wang

Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations.

Classification counterfactual +1

The DongNiao International Birds 10000 Dataset

1 code implementation21 Sep 2020 Jian Mei, Hao Dong

DongNiao International Birds 10000 (DIB-10K) is a challenging image dataset which has more than 10 thousand different types of birds.

BIG-bench Machine Learning Image Classification

Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control

no code implementations20 Sep 2020 Qingrui Zhang, Hao Dong, Wei Pan

More importantly, the existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system from a control-theoretic perspective, so the learned control polices are highly possible to generate abnormal or dangerous behaviors in real applications.

Multi-agent Reinforcement Learning reinforcement-learning +1

Efficient Reinforcement Learning Development with RLzoo

1 code implementation18 Sep 2020 Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong

RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).

reinforcement-learning Reinforcement Learning (RL)

Generative 3D Part Assembly via Dynamic Graph Learning

3 code implementations NeurIPS 2020 Jialei Huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong

Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.

Graph Learning Pose Estimation +1

Role-Wise Data Augmentation for Knowledge Distillation

1 code implementation ICLR 2020 Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, Hao Dong

To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.

Data Augmentation Knowledge Distillation

DLGAN: Disentangling Label-Specific Fine-Grained Features for Image Manipulation

1 code implementation22 Nov 2019 Guanqi Zhan, Yihao Zhao, Bingchan Zhao, Haoqi Yuan, Baoquan Chen, Hao Dong

By mapping the discrete label-specific attribute features into a continuous prior distribution, we leverage the advantages of both discrete labels and reference images to achieve image manipulation in a hybrid fashion.

Attribute Image Manipulation +1

Gear Training: A new way to implement high-performance model-parallel training

no code implementations11 Jun 2018 Hao Dong, Shuai Li, Dongchang Xu, Yi Ren, Di Zhang

The training of Deep Neural Networks usually needs tremendous computing resources.

Generative Creativity: Adversarial Learning for Bionic Design

no code implementations19 May 2018 Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo

Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

no code implementations20 Nov 2017 Hao Dong, Chao Wu, Zhen Wei, Yike Guo

However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.

Anomaly Detection Decision Making +2

TensorLayer: A Versatile Library for Efficient Deep Learning Development

2 code implementations26 Jul 2017 Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others.

Management

Semantic Image Synthesis via Adversarial Learning

2 code implementations ICCV 2017 Hao Dong, Simiao Yu, Chao Wu, Yike Guo

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.

Image Generation Image Manipulation

Deep De-Aliasing for Fast Compressive Sensing MRI

no code implementations19 May 2017 Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.

Compressive Sensing De-aliasing +1

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

no code implementations10 May 2017 Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo

In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent.

Brain Tumor Segmentation Image Segmentation +2

I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation

no code implementations20 Mar 2017 Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo

We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art.

Data Augmentation Image Captioning +2

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

8 code implementations12 Mar 2017 Akara Supratak, Hao Dong, Chao Wu, Yike Guo

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

EEG Electroencephalogram (EEG) +1

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

no code implementations10 Jan 2017 Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo

It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.

Translation Unsupervised Image-To-Image Translation

Mixed Neural Network Approach for Temporal Sleep Stage Classification

no code implementations15 Oct 2016 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo

Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Classification EEG +2

DropNeuron: Simplifying the Structure of Deep Neural Networks

1 code implementation23 Jun 2016 Wei Pan, Hao Dong, Yike Guo

We proposed regularisers which support a simple mechanism of dropping neurons during a network training process.

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